This document is the summary of the Introduction to R workshop.
All correspondence related to this document should be addressed to:
Omid Ghasemi (Macquarie University, Sydney, NSW, 2109, AUSTRALIA)
Email: omidreza.ghasemi@hdr.mq.edu.auThe aim of the study is to test if simple arguments are more effective in belief revision than more complex arguments. To that end, we present participants with an imaginary scenario (two alien creatures on a planet) and a theory (one creature is predator and the other one is prey) and we ask them to rate the likelihood truth of the theory based on a simple fact (We adapted this method from Gregg et al.,2017; see the original study here). Then, in a between-subject manipulation, participants will be presented with either 6 simple arguments (Modus Ponens conditionals) or 6 more complex arguments (Modus Tollens conditionals), and they will be asked to rate the likelihood truth of the initial theory on 7 stages.
The first stage is the base rating stage. The next three stages include supportive arguments of the theory and the last three arguments include disproving arguments of the theory. We hypothesized that the group with simple arguments shows better persuasion (as it reflects in higher ratings for the supportive arguments) and better dissuasion (as it reflects in lower ratings for the opposing arguments).
In the last part of the study, participants will be asked to answer several cognitive capacity/style measures including thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales. We hypothesized that cognitive ability, cognitive style, and open-mindedness are positive predictors of persuasion and dissuasion. These associations should be more pronounced for participants in the group with complex arguments because the ability and willingness to engage in deliberative thinking may favor participants to assess the underlying logical structure of those arguments. However, for participants in the simple group, the logical structure of arguments is more evident, so participants with lower ability can still assess the logical status of those arguments.
Thus, our hypotheses for this experiment are as follows:
Participants in the group with simple arguments have higher ratings for supportive arguments (They are more easily persuaded than those in the group with complex arguments).
Participants in the group with simple arguments have lower ratings for opposing arguments (They are more easily dissuaded than those in the group with complex arguments).
There are significant associations between thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales with both persuasion and dissuasion indexes in each group and in the entire sample. The relationship between these measures should be stronger, although not significantly, for participants in the group with complex arguments.
First, we need to design the experiment. For this experiment, we use online platforms for data collection. There are several options such as Gorilla, JSpsych, Qualtrics, psychoJS (pavlovia), etc. Since we do not need any reaction time data, we simply use Qualtrics. For an overview of different lab-based and online platforms, see here.
Next, we need to decide on the number of participants (sample size). For this study, we do not sue power analysis since we cannot access more than 120 participants. However, it is highly suggested calculate sample size using power estimation. You can find some nice tutorials on how to do that here, here, and here.
After we created the experiment and decided on the sample size, the next step is to preresigter the study. However, it would be better to do a pilot with 4 or 5 participants, clean all the data, do the desired analysis, and then pre-register the analysis and those codes. You can find the preregistration form for the current study here.
Finally, we need to restructure our project in a tidy folder with different sub-folders. Having a clean and tidy folder structure can save us! There are different formats of folder structure (for example, see here and here), but for now, we use the following structure:
# load libraries
library(tidyverse)
library(here)
library(janitor)
library(broom)
library(afex)
library(emmeans)
library(knitr)
library(kableExtra)
library(ggsci)
library(patchwork)
library(skimr)
# install.packages("devtools")
# devtools::install_github("easystats/correlation")
library("correlation")
options(scipen=999) # turn off scientific notations
options(contrasts = c('contr.sum','contr.poly')) # set the contrast sum globally
options(knitr.kable.NA = '')
Artwork by Allison Horst: https://github.com/allisonhorst/stats-illustrations
R can be used as a calculator. For mathematical purposes, be careful of the order in which R executes the commands.
10 + 10
## [1] 20
4 ^ 2
## [1] 16
(250 / 500) * 100
## [1] 50
R is a bit flexible with spacing (but no spacing in the name of variables and words)
10+10
## [1] 20
10 + 10
## [1] 20
R can sometimes tell that you’re not finished yet
10 +
How to create a variable? Variable assignment using <- and =. Note that R is case sensitive for everything
pay <- 250
month = 12
pay * month
## [1] 3000
salary <- pay * month
Few points in naming variables and vectors: use short, informative words, keep same method (e.g., not using capital words, use only _ or . ).
Function is a set of statements combined together to perform a specific task. When we use a block of code repeatedly, we can convert it to a function. To write a function, first, you need to define it:
my_multiplier <- function(a,b){
result = a * b
return (result)
}
This code do nothing. To get a result, you need to call it:
my_multiplier (a=2, b=4)
## [1] 8
# or: my_multiplier (2, 4)
We can set a default value for our arguments:
my_multiplier2 <- function(a,b=4){
result = a * b
return (result)
}
my_multiplier2 (a=2)
## [1] 8
# or: my_multiplier (2)
# or: my_multiplier (2, 6)
Fortunately, you do not need to write everything from scratch. R has lots of built-in functions that you can use:
round(54.6787)
## [1] 55
round(54.5787, digits = 2)
## [1] 54.58
Use ? before the function name to get some help. For example, ?round. You will see many functions in the rest of the workshop.
function class() is used to show what is the type of a variable.
TRUE, FALSE can be abbreviated as T, F. They has to be capital, ‘true’ is not a logical data:class(TRUE)
## [1] "logical"
class(F)
## [1] "logical"
class(2)
## [1] "numeric"
class(13.46)
## [1] "numeric"
class("ha ha ha ha")
## [1] "character"
class("56.6")
## [1] "character"
class("TRUE")
## [1] "character"
Can we change the type of data in a variable? Yes, you need to use the function as.---()
as.numeric(TRUE)
## [1] 1
as.character(4)
## [1] "4"
as.numeric("4.5")
## [1] 4.5
as.numeric("Hello")
## Warning: NAs introduced by coercion
## [1] NA
Vector: when there are more than one number or letter stored. Use the combine function c() for that.
sale <- c(1, 2, 3,4, 5, 6, 7, 8, 9, 10) # also sale <- c(1:10)
sale <- c(1:10)
sale * sale
## [1] 1 4 9 16 25 36 49 64 81 100
Subsetting a vector:
days <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
days[2]
## [1] "Sunday"
days[-2]
## [1] "Saturday" "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"
days[c(2, 3, 4)]
## [1] "Sunday" "Monday" "Tuesday"
Create a vector named my_vector with numbers from 0 to 1000 in it:
my_vector <- (0:1000)
mean(my_vector)
## [1] 500
median(my_vector)
## [1] 500
min(my_vector)
## [1] 0
range(my_vector)
## [1] 0 1000
class(my_vector)
## [1] "integer"
sum(my_vector)
## [1] 500500
sd(my_vector)
## [1] 289.1081
List: allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other list.
my_list = list(sale, 1, 3, 4:7, "HELLO", "hello", FALSE)
my_list
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4 5 6 7
##
## [[5]]
## [1] "HELLO"
##
## [[6]]
## [1] "hello"
##
## [[7]]
## [1] FALSE
Factor: Factors store the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character. For example, variable gender with “male” and “female” entries:
gender <- c("male", "male", "male", " female", "female", "female")
gender <- factor(gender)
R now treats gender as a nominal (categorical) variable: 1=female, 2=male internally (alphabetically).
summary(gender)
## female female male
## 1 2 3
Question: why when we ran the above function i.e. summary(), it showed three and not two levels of the data? Hint: run ‘gender’.
gender
## [1] male male male female female female
## Levels: female female male
So, be careful of spaces!
Create a gender factor with 30 male and 40 females (Hint: use the rep() function):
gender <- c(rep("male",30), rep("female", 40))
gender <- factor(gender)
gender
## [1] male male male male male male male male male male
## [11] male male male male male male male male male male
## [21] male male male male male male male male male male
## [31] female female female female female female female female female female
## [41] female female female female female female female female female female
## [51] female female female female female female female female female female
## [61] female female female female female female female female female female
## Levels: female male
There are two types of categorical variables: nominal and ordinal. How to create ordered factors (when the variable is nominal and values can be ordered)? We should add two additional arguments to the factor() function: ordered = TRUE, and levels = c("level1", "level2"). For example, we have a vector that shows participants’ education level.
edu<-c(3,2,3,4,1,2,2,3,4)
education<-factor(edu, ordered = TRUE)
levels(education) <- c("Primary school","high school","College","Uni graduated")
education
## [1] College high school College Uni graduated
## [5] Primary school high school high school College
## [9] Uni graduated
## Levels: Primary school < high school < College < Uni graduated
We have a factor with patient and control values. Here, the first level is control and the second level is patient. Change the order of levels, so patient would be the first level:
health_status <- factor(c(rep('patient',5),rep('control',5)))
health_status
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: control patient
health_status_reordered <- factor(health_status, levels = c('patient','control'))
health_status_reordered
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: patient control
Finally, can you relabel both levels to uppercase characters? (Hint: check ?factor)
health_status_relabeled <- factor(health_status, levels = c('patient','control'), labels = c('Patient','Control'))
health_status_relabeled
## [1] Patient Patient Patient Patient Patient Control Control Control
## [9] Control Control
## Levels: Patient Control
Matrices: All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. It can be created using a vector input to the matrix function.
my_matrix = matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, ncol = 3)
my_matrix
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Data frames: (two-dimensional objects) can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type. Let’s create a dataframe:
id <- 1:200
group <- c(rep("Psychotherapy", 100), rep("Medication", 100))
response <- c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5))
my_dataframe <-data.frame(Patient = id,
Treatment = group,
Response = response)
We also could have done the below
my_dataframe <-data.frame(Patient = c(1:200),
Treatment = c(rep("Psychotherapy", 100), rep("Medication", 100)),
Response = c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5)))
In large data sets, the function head() enables you to show the first observations of a data frames. Similarly, the function tail() prints out the last observations in your data set.
head(my_dataframe)
tail(my_dataframe)
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 31.70881 |
| 2 | Psychotherapy | 29.80296 |
| 3 | Psychotherapy | 23.52650 |
| 4 | Psychotherapy | 22.40651 |
| 5 | Psychotherapy | 28.24076 |
| 6 | Psychotherapy | 33.40219 |
| Patient | Treatment | Response | |
|---|---|---|---|
| 195 | 195 | Medication | 30.27847 |
| 196 | 196 | Medication | 27.80703 |
| 197 | 197 | Medication | 23.65503 |
| 198 | 198 | Medication | 26.56304 |
| 199 | 199 | Medication | 17.95121 |
| 200 | 200 | Medication | 29.54396 |
Similar to vectors and matrices, brackets [] are used to selects data from rows and columns in data.frames:
my_dataframe[35, 3]
## [1] 23.76307
How can we get all columns, but only for the first 10 participants?
my_dataframe[1:10, ]
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 31.70881 |
| 2 | Psychotherapy | 29.80296 |
| 3 | Psychotherapy | 23.52650 |
| 4 | Psychotherapy | 22.40651 |
| 5 | Psychotherapy | 28.24076 |
| 6 | Psychotherapy | 33.40219 |
| 7 | Psychotherapy | 22.63793 |
| 8 | Psychotherapy | 25.07547 |
| 9 | Psychotherapy | 34.68674 |
| 10 | Psychotherapy | 22.50409 |
How to get only the Response column for all participants?
my_dataframe[ , 3]
## [1] 31.708813 29.802960 23.526499 22.406512 28.240756 33.402191 22.637934
## [8] 25.075474 34.686743 22.504094 31.517422 31.264633 26.726864 33.805724
## [15] 28.712035 29.206099 32.529914 35.050625 31.384180 33.971724 33.693739
## [22] 23.990873 28.518247 26.133280 28.565941 39.463296 29.229717 23.608218
## [29] 31.647677 30.622080 32.411563 28.739493 28.645108 34.801284 23.763072
## [36] 26.795876 32.982249 28.448125 36.993561 36.064960 24.097104 28.237377
## [43] 27.844498 34.312489 27.544267 30.788393 31.458230 26.740752 36.949616
## [50] 26.317241 31.908821 25.665635 27.485842 39.576472 35.183187 26.220313
## [57] 37.106567 19.481076 36.338665 43.495743 34.346674 29.216833 30.594568
## [64] 22.948427 32.447711 24.776949 29.147548 34.796360 21.363509 31.261505
## [71] 27.672622 27.813903 30.288316 34.701551 28.777852 29.325077 29.505490
## [78] 27.207470 34.332822 27.351041 31.376275 23.737676 30.326625 26.727234
## [85] 32.239994 25.803207 28.133668 29.749694 25.755813 25.832310 36.035813
## [92] 40.370852 29.732889 33.913627 38.481958 30.521629 36.591710 36.253998
## [99] 36.839956 33.890120 28.825717 25.953143 31.525600 25.307904 30.639792
## [106] 29.586151 28.806757 28.620229 24.976085 23.650821 26.717328 19.819130
## [113] 26.303396 25.867117 22.057393 23.588511 19.602866 34.365061 29.845442
## [120] 29.824766 22.578831 27.138616 26.906843 19.468686 20.513763 23.623201
## [127] 27.112159 21.139209 26.392881 32.285443 28.811703 25.948436 19.244730
## [134] 20.519502 35.670476 22.532543 19.606732 14.912368 22.798981 29.778866
## [141] 37.605444 26.703724 22.106022 25.594774 25.625431 24.984104 19.501609
## [148] 12.321973 26.521236 33.397335 25.515591 30.697933 29.256076 21.368050
## [155] 34.022232 18.607188 30.729220 24.540642 13.381217 29.403218 20.353568
## [162] 7.282473 20.463582 30.552557 22.783704 28.032865 24.552576 35.305996
## [169] 27.477994 22.476527 22.454651 27.698763 31.783351 34.063251 17.934524
## [176] 31.016026 21.967420 16.280651 19.184221 23.220447 27.296864 29.996700
## [183] 21.959125 24.456886 20.480283 23.785494 25.151047 26.180715 23.331798
## [190] 27.081697 30.603483 20.888471 22.475623 24.376636 30.278467 27.807029
## [197] 23.655029 26.563044 17.951207 29.543957
Another easier way for selecting particular items is using their names that is more helpful than number of the rows in large data sets:
my_dataframe[ , "Response"]
# OR:
my_dataframe$Response